2020
DOI: 10.12694/scpe.v21i3.1783
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A Machine Translation System from Hindi to Sanskrit Language using Rule based Approach

Abstract: Machine Translation is an area of Natural Language Processing which can replace the laborious task of manual translation. Sanskrit language is among the ancient Indo-Aryan languages. There are numerous works of art and literature in Sanskrit. It has also been a medium for creating treatise of philosophical work as well as works on logic, astronomy and mathematics. On the other hand, Hindi is the most prominent language of India. Moreover,it is among the most widely spoken languages across the world. This paper… Show more

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Cited by 15 publications
(3 citation statements)
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“…After parsing the sentence in the source language, an transitional representation, like a parse tree or abstract representation, is generated. Figure 3 shows a general architecture of a RBMT system [6]. RBMT systems are again classified into Direct Translation, Transfer-Based Translation, and Interlingua categories based on the type of transitional representation they use.…”
Section: A Rule-based Machine Translation (Rbmt)mentioning
confidence: 99%
“…After parsing the sentence in the source language, an transitional representation, like a parse tree or abstract representation, is generated. Figure 3 shows a general architecture of a RBMT system [6]. RBMT systems are again classified into Direct Translation, Transfer-Based Translation, and Interlingua categories based on the type of transitional representation they use.…”
Section: A Rule-based Machine Translation (Rbmt)mentioning
confidence: 99%
“…Wang (2024) remarked that the early approaches of MT were rule-based systems that relied on dictionaries, grammar, and transfer rules to generate translations. The study of Bhadwal et al (2020) utilized Rule-Based Machine Translation (RBMT) to translate prominent language features of Hindi and Sanskrit, effectively addressing the challenge of polysemy in verb translation. While RBMT could produce high-quality translation, these systems needed improvement in handling ambiguity and idiosyncrasies of new language pairs or domains (Harsha et al, 2022;De Martino et al, 2023).…”
Section: Evolution Of Machine Translationmentioning
confidence: 99%
“…NLP is commonly used for text mining, machine translation, and automated question-response. With the exponential growth of AI and computational technology, current NLP research and implementation also includes AI-base machine learning, data mining, deep learning and agent ontology [23]. NLP can be divided into two broad areas: core or fundamental, and applications.…”
Section: Natural Language Processingmentioning
confidence: 99%